This study addresses the hypothesis that bedding selection on UK dairy farms is captive to existing manure management infrastructure rather than being driven primarily by clinical evidence of material performance. The management of dairy cow resting areas is a critical determinant of animal welfare and farm productivity. While academic research often focuses on the biological properties of bedding materials in isolation, commercial selection is frequently constrained by fixed infrastructure and logistical realities. A comprehensive online survey was conducted among 228 dairy farms across the UK (median herd size: 194 lactating cows) to evaluate the interplay between housing infrastructure, bedding selection and manure management. Data were collected on barn design, flooring, manure transport mechanisms and the economic and operational management of primary bedding materials. Cubicle (freestall) housing was near-universal (97%), with 77% of farms using hybrid systems consisting of a synthetic mattress topped with a loose bedding layer. Sawdust was the predominant material (56%), followed by sand (20%) and straw (15%). Although sand was recognised for its clinical benefits, 11% of current sand users expressed an intention to transition away from the material, citing mechanical wear on machinery and slurry sedimentation as primary drivers. Straw use exhibited a distinct bimodal distribution, with application rates nearly 10 times higher in loose-housed yards (12–14 kg/cow/d) than in cubicle systems (1.6 kg/cow/d). Notably, 70% of respondents were unwilling to pay a financial premium for sustainably sourced bedding, prioritising operational compatibility and animal hygiene over environmental labels. These results demonstrate that bedding choice in the UK is constrained by existing manure management infrastructure. Future research and policy must bridge the gap between clinical excellence and mechanical viability, focusing on engineering solutions that allow farms to use high-comfort materials without compromising logistical efficiency.

Background: Electronic surveillance for hospital-onset sepsis using CDC’s Adult Sepsis Event definition could provide an efficient and objective method to identify a broad array of serious healthcare-associated infections, many of which are missed through current reporting processes. We developed risk adjustment models of varying complexity to support facility-level comparison of hospital-onset sepsis rates, evaluated trade-offs between model performance and feasibility, and quantified residual inter-facility variation that may reflect gaps in care. Methods: We conducted a retrospective study of adults hospitalized for <3 days within 113 community hospitals between 2022-2023. Hospital-onset Adult Sepsis Events (HO-ASEs) occurring on day 4 or later were identified using updated CDC surveillance criteria. We used logistic regression to develop three risk adjustment models of increasing complexity using covariates from administrative and electronic health record data: basic model (hospital and aggregate patient descriptors), intermediate model (replacing aggregate patient descriptors with patient-level descriptors), and maximal model (adding detailed physiologic and clinical data from hospital days 1-3; Figure 1). We evaluated model performance using Area Under Receiver Operating Curve (AUROC), calculated hospital-level Standardized Infection Ratios (SIRs) for each model and assessed concordance in hospital rankings using Kendall’s tau coefficient (τ). Results: The cohort included 1,557,252 hospitalizations of <3 days, of which 24,169 (1.6%) met HO-ASE criteria and 8,500 (35.2%) died in-hospital. The basic model had limited discrimination (AUROC 0.589, 95% CI, 0.585-0.593). Adding patient-level characteristics to form the intermediate model markedly improved performance (AUROC 0.839, 95% CI 0.836-0.841), with further inclusion of detailed clinical data in the maximal model yielding modest additional improvement (AUROC 0.850, 95% CI 0.848-0.853). Concordance between hospital rankings derived from the crude or basic risk-adjusted HO-ASE rates versus rankings derived from the intermediate or maximal models was moderate (τ 0.40-0.51) whereas concordance between rankings derived from the intermediate vs. maximal models was high (τ 0.86). There was a wide distribution of HO-ASE SIRs across facilities even after risk adjustment using the maximal model (Figure 2), with high signal-to-noise ratios and good calibration. Conclusions: Risk adjustment models incorporating hospital characteristics and patient-level data perform well and might explain substantial variability in HO-ASE rates between facilities. The persistence of residual variability after highly detailed adjustment may reflect differences in care processes, suggesting that risk-adjusted HO-ASE comparisons can help identify gaps and opportunities in the prevention of severe healthcare-associated infections. Our findings support the use of HO-ASE as an electronic, scalable, risk-adjusted metric for facility-level benchmarking to inform quality improvement initiatives.

Antibiotic overprescribing remains common in pediatric outpatient care. We evaluated Medicaid claims–based provider report cards as a scalable stewardship tool. Quarterly audit and feedback improved guideline concordance, diagnostic appropriateness, and reduced cefdinir use; highlighting claims-based electronic feedback as a sustainable outpatient antimicrobial stewardship strategy suitable for statewide implementation.

Drylands cover 41% of Earth’s land surface, support 36% of the global population and contribute 60% of global food production. Despite these ecosystems’ importance and high vulnerability to droughts and heatwaves, drylands remain some of the most understudied systems on Earth. Monitoring drylands is challenging due to their complex ecosystem structure of visible soil mixed with diverse plant species that respond rapidly to weather and climate. In 2023 and 2024, a NASA scoping study was conducted for a proposed dryland terrestrial ecology field campaign called Adaptation and Response in Drylands (ARID). Thereafter, the NASA ARID scoping team submitted their campaign proposal to NASA Headquarters, providing a study design for how field, aircraft and satellite measurements, as well as modeling, could address the most critical fundamental and applied science questions in drylands. The extensive strategic vision was created by and for the drylands research community, including remote sensors, modelers, experimentalists and ecologists from across the world, and the overall approach can be further utilized and altered for different uses and data information needs. Here, we summarize the final ARID research agenda, including its main objectives, field campaign strategy, data end-user support strategy, and U.S. and global community engagement.

Objectives/Goals: Ovarian cancer is the most lethal gynecologic cancer with emergence of recurrent and resistant disease being frequent. We sought to identify novel ways to effectively kill chemotherapy-resistant forms of ovarian cancer and to prevent recurrence following curative intent with surgical and adjuvant regimens. Methods/Study Population: Our group identified CTPS1 as a novel dependency in triple negative breast cancer (TNBC) and ovarian cancer. We found CTPS1 was highly expressed in breast and ovarian tumor tissues and therapy-resistant models. We demonstrated siRNA knockdown of CTPS1 reduced proliferation in sensitive and resistant cell lines. In collaboration with Step Pharma, who developed the first CTPS1 inhibitor (STP938), we performed genome-wide CRISPR KO screens on STP938-sensitive and -resistant cells. Top synthetic lethal hits identified included TSC1, TSC2, and PPP2R2A. Synergy assays were conducted with STP938 in combination with mTOR inhibitors (Everolimus, Sirolimus, and Temsirolimus) and the PPP2R2A inhibitor LB-100 to evaluate the therapeutic benefit of combination therapies. Results/Anticipated Results: We identified CTPS1 as a novel vulnerability in many ovarian cancer cell line models, including models of chemotherapy and/or PARP inhibitor resistance. The first-in-class CTPS1 inhibitor (STP938) was shown to be a highly effective anticancer agent in sensitive and resistant forms of ovarian cancer, both in vitro and in vivo. Genome-wide CRISPR KO screens identified central components of the mTOR pathway (TSC1 and TSC2), as well as PPP2R2A, as synthetic lethal targets in the setting of STP938 exposure. We anticipate that combinatorial treatment of mTOR inhibitors and PP2R2A inhibitors with STP938 will further enhance efficacy and reduce the risk of recurrence when used in the adjuvant treatment of ovarian cancer patients. Discussion/Significance of Impact: This study will validate synthetic lethal interactions from our CRISPR screen, reveal genes that could be mutated in ovarian cancer patients that may be driving resistance to STP938, and uncover combination therapies that can be used to increase efficacy in ovarian cancer patients treated with STP938.

Objectives/Goals: Summarize literature on treatment outcomes among postpartum women with OUD. Describe a model Assessment, Decision, Adaptation, Production, Topical Experts, Integration, Training, and Testing (ADAPT-ITT) guiding the adaptation of Baby & Me. Assess preliminary effectiveness and implementation outcomes of the adapted version of Baby & Me. Methods/Study Population: We used the ADAPT-ITT model to tailor Baby & Me, a brief education and skills-based parenting program, for postpartum women receiving MOUD in the Women’s Mental Health Program (WMHP) at UAMS. Originally delivered for Women, Infants, & Children offices, Baby & Me targets parenting stress and well-being to prevent child abuse/neglect. Adaptations focused on the unique parenting-related needs of postpartum women receiving MOUD. We will pilot the adapted program with 10 participants, receiving care at the WMHP using a pre- and post-test design with surveys and interviews to assess preliminary effectiveness (i.e., perceived parenting stress, postpartum depression, and substance use) and implementation outcomes (acceptability, appropriateness, and feasibility). Results/Anticipated Results: We anticipate that participation in the Baby & Me program will be associated with reductions in perceived parenting stress, postpartum depression symptoms, and substance use from baseline to follow-up among postpartum parenting women with opioid use disorder. Qualitative interviews are expected to reveal that recipients find the intervention to be acceptable, appropriate, and feasible for delivery in outpatient treatment settings, yet inform further refinements prior to larger-scale testing. Discussion/Significance of Impact: Parenting programs in outpatient treatment settings can potentially improve maternal mental health, strengthen mother–infant bonds, and recovery outcomes. Yet most are home-based, lengthy, and cover a broad range of child developmental stages. Embedding brief, tailored programs into clinical care may boost acceptability and impact.

This article highlights the relevance of the humanities to psychiatric discussions. It considers Thomas De Quincey’s autobiographical Confessions of an English Opium-Eater (1821), where the author charts his opium experiences and dependence. The text provides an intersection of subjective and objective evaluations of drug use, and records experiences undertaken before establishment of psychiatric disciplines. De Quincey’s self-analysis anticipates psychoanalytic techniques and psychopharmacology. This article raises questions applicable to the medical humanities, such as complexities of the patient’s voice, therapeutic creativity and how literature functions as a record of phenomenology. It underlines the relevance of literature for psychiatric practitioners.

The traditional account of the criminal trial holds that its fundamental purpose is to search for the truth—that is, the truth of whether the accused factually committed the alleged crime. However, purely truth-seeking accounts, as well as more nuanced side-constraint and pluralist accounts, fail to adequately explain the relationship between the epistemic principles and those of political morality shaping the criminal trial. In response, this article proposes that we understand the criminal trial first and foremost in terms of its purpose as a public procedure concerned with the legitimate use of coercive state powers against a particular person. Specifically, the criminal trial is a procedure that calls upon the state to provide a public justification for exercising its criminal law powers to convict and punish the accused. This account preserves the importance of establishing factual guilt because doing so is an essential part of the state’s justificatory burden.

Precision applications of herbicides are gaining interest as a sustainable approach to managing turfgrass pests. For instance, controlling turfgrass weeds with precision application could effectively reduce herbicide volume without sacrificing weed control. Machine learning models have been a common method for precision application, but machine learning requires intensive labor and expertise to collect and label imagery. The objective of this study was to develop and test a new system that uses the dark green color index (DGCI) to precisely apply glyphosate to detect and spray winter weeds in dormant bermudagrass turf. For this study, a sprayer prototype was constructed that used machine vision and DGCI. The prototype consisted of three primary components: 1) a camera that streamed video frames, 2) a control system that stored computer code focused on the integration of DGCI, and 3) solenoid valves that activated upon detection of winter weeds growing in dormant bermudagrass. Four field trials with different weed species and weed densities were established to test the DGCI system among traditional application methods (i.e., broadcast application and manual spot application with a backpack sprayer). In the lowest weed density scenario, the DGCI system accurately detected and sprayed 90% of the weed population, reducing herbicide volume by 62% compared to a broadcast application. Additionally, the DGCI system required less time for treatment than the spot application with a backpack sprayer. The results from these trials suggest that vegetative indices, such as DGCI, have potential in dormant bermudagrass systems to optimize herbicide volume.